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Abstract:

Novel compositions, methods, assays and kits directed to a diagnostic
panel for Alzheimer's disease are provided. In one embodiment, the
diagnostic panel includes one or more proteins associated with
Alzheimer's disease.

Claims:

1. A diagnostic Alzheimer's disease panel comprising one or more proteins
associated with Alzheimer's disease.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent
Application No. 61/417,871, filed Nov. 29, 2010 which is incorporated
herein by reference.

BACKGROUND

[0002] One aim of modern diagnostic medicine is to better identify
sensitive diagnostic methods to determine changes in health status. A
variety of diagnostic assays and computational methods are used to
monitor health. Improved sensitivity is an important goal of diagnostic
medicine. Early diagnosis and identification of disease and changes in
health status may permit earlier intervention and treatment that will
produce healthier and more successful outcomes for the patient.
Diagnostic markers are important for prognosis, diagnosis and monitoring
disease and changes in health status. In addition, diagnostic markers are
important for predicting response to treatment and selecting appropriate
treatment and monitoring response to treatment.

[0003] Many diagnostic markers are identified in the blood. However,
identification of appropriate diagnostic markers is challenging due to
the number, complexity and variety of proteins in the blood.
Distinguishing between high abundance and low abundance detectable
markers requires novel methods and assays to determine the differences
between normal levels of detectable markers and changes of such
detectable markers that are indicative of changes in health status. The
present invention provides novel compositions, methods and assays to
fulfill these and other needs.

[0005] In another embodiment, the diagnostic Alzheimer's disease panel is
a set of seven proteins that includes F13A1, PON1, ITIH1, CLU, APOD, GSN
and APOA4. In another embodiment, the diagnostic Alzheimer's disease
panel is a set of three proteins that includes GSN, F13A1 and PON1.

[0006] In another embodiment, a diagnosis of Alzheimer's disease may be
made based on the detection of differential expression or differential
presence of four or more significant transitions that are associated with
the Alzheimer's disease panel. The Alzheimer's disease diagnosis may be a
determination of whether a patient is experiencing the early stages of
the disease.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 are representative images of a brain with diagnosed
Alzheimer's disease having substantial loss of brain tissue (left) as
compared to a normally aged brain in a normal elderly control (NEC)
(right).

[0008] FIG. 2 is a graph showing the delay in a patient's decline in
quality of life as a result of earlier diagnosis and treatment of
Alzheimer's disease.

[0009] FIG. 3 is a graph showing the delay in admission to long-term care
and shorter stays in such facilities as a result of early diagnosis and
treatment of Alzheimer's disease.

[0010] FIG. 4 is a regression plot illustrating the correlation of the
blood protein biomarkers described herein to mini mental state evaluation
(MMSE) score (r2=0.75, p<0.0022).

[0011] FIG. 5 is a schematic illustrating MRM technology related to the
selected peptides and transitions for a target protein, Protein X.

[0015] FIG. 9 is a receiver operating characteristic (ROC) curve for
illustrating the diagnostic performance of the multivariate Alzheimer's
disease panel (AUC=0.82) as determined by the significant transitions
listed in FIG. 8.

[0016] FIG. 10 is a receiver operating characteristic (ROC) curve for
illustrating the diagnostic performance of the 8 individual significant
transitions for four peptides (TGAQELLR, LIASMSSDSLR, IQNILTEEPK and
STVLTIPEIIIK; two transitions per peptide) and a receiver operating
characteristic (ROC) curve for illustrating the diagnostic performance of
a 3-protein Alzheimer's disease panel (GSN, F13A1 and PON1; AUC=0.80)
based on the combined performance of the 8 individual significant
transitions.

[0018] The present disclosure provides novel compositions, methods, assays
and kits directed to a diagnostic panel for Alzheimer's disease panel. In
one embodiment, the diagnostic panel includes one or more proteins
associated with Alzheimer's disease. The diagnostic panel can be used for
prognosis and diagnosis, monitoring treatment and monitoring response to
treatment.

[0021] In another embodiment, the diagnostic Alzheimer's disease panel is
a set of seven proteins that includes F13A1, PON1, ITIH1, CLU, APOD, GSN
and APOA4. In yet another embodiment, the diagnostic Alzheimer's disease
panel is a set of three proteins: coagulation factor XIIIa (F13A1),
paraoxonase 1 (PON1) and gelsolin (GSN). The Alzheimer's disease panels
identified herein are sensitive and accurate diagnostic tools that can be
measured in a biological sample. The Alzheimer's disease panels include a
group or set of Alzheimer's disease-specific proteins that have been
associated with the disease and have been detected in biological samples
of subjects who have Alzheimer's disease and normal control populations.

[0022] The diagnostic panels of the present disclosure can be used for
diagnosing Alzheimer's disease in a subject. As used herein, the term
"subject" refers to any animal (e.g., a mammal), including but not
limited to humans, non-human primates, rodents, dogs, pigs, and the like.
In one aspect, the Alzheimer's disease panels may be used to diagnose
Alzheimer's disease before the disease is too far advanced for
intervention (see FIG. 1). Currently, early diagnosis of Alzheimer's
disease is based on a patient exhibiting minimal cognitive impairment
(MCI) and ruling out other central nervous system neuropathies, however,
there are no established diagnostic tools or universal standards for
classifying early stages of the disease. Early intervention in the
development of Alzheimer's disease can delay a patient's decline in
quality of life (FIG. 2) and can delay admission to long-term care and
shorten stays in such facilities (FIG. 3).

[0023] In one embodiment, a method for diagnosing Alzheimer's disease
includes obtaining a biological sample (e.g., a blood, plasma or serum
sample) from a subject having or suspected of having a form of cognitive
impairment or dementia and determining whether a differential expression
or differential presence of one or more proteins, peptides or transitions
associated with the Alzheimer's disease panels described herein. Such a
method may further include a system for distinguishing Alzheimer's
disease from other forms of dementia or cognitive impairment to allow
early detection of Alzheimer's disease and risk factors. For example,
methods described herein may be used to classify or distinguish between
Alzheimer's disease from a normal aging effect on cognitive function
(i.e., diseased patients as compared to normal elderly controls, (NEC)),
Untreated Alzheimer's disease (UTAD), as compared to Treated Alzheimer's
Disease (TTAD), Alzheimer's disease as compared to mild cognitive
impairment, and additional comparisons between other stages of cognitive
disorders.

[0024] In some embodiments, the method for diagnosing Alzheimer's disease
as described above may optionally include administration of a mini mental
state examination (MMSE) for validation of a diagnosis made based on the
Alzheimer's disease panels. An MMSE is a questionnaire that tests for
cognitive impairment and is often used to screen for dementia. An MMSE,
when used in combination with the methods described herein, may be used
to validate the results of the methods for diagnosing Alzheimer's disease
based on the Alzheimer's disease panels described herein. As shown in
FIG. 4, the biomarkers associated with the Alzheimer's disease panels are
correlated to the MMSE scores.

[0025] The diagnostic Alzheimer's disease panels used in the methods
described herein may be used to diagnose Alzheimer's disease and may be
used to distinguish the development of Alzheimer's disease from less
severe forms of dementia or may by used to rule out other forms of
cognitive impairment or dementia. Examples of cognitive disorders or
dementia that may be ruled out by the methods that use the Alzheimer's
disease panels described herein include, include, but are not limited to
normal aging, Parkinson's disease, vascular dementia, dementia with Lewy
bodies, progressive supranuclear palsy, corticobasal degeneration,
frontotemporal lobular degeneration and Bechet's disease.

[0026] According to the methods described herein, a diagnosis of
Alzheimer's disease may be made based on the detection of one or more
proteins, peptides or transitions that are differentially present or
differentially expressed in a biological sample (e.g., blood, plasma or
serum). In one embodiment, the one or more peptides or transitions are
associated with the proteins of the Alzheimer's disease panels (i.e.,
F13A1, PON1, ITIH1, CLU, APOD, GSN and APOA4).

[0027] In one embodiment, a diagnosis of Alzheimer's disease may be made
based on the detection of one or more significant transitions in a
biological sample (e.g., blood, plasma or serum). In one aspect the one
or more significant transitions are selected from LIASMSSDSLR
(590.3-1066.3), LIASMSSDSLR (590.3-953.2), GSLVQASEANLQAAQDFVR
(1002.5-1448.6), GSLVQASEANLQAAQDFVR (1002.5-1232.6), IQNILTEEPK
(592.8-829.4), IQNILTEEPK (592.8-943.4), EIQNAVNGVK (536.3-417.2),
TGAQELLR (444.2-530.3), TGAQELLR (444.2-658.4), VLNQELR (436.2-659.3),
VLNQELR (436.2-772.4), ALVQQMEQLR (608.3-932.5), ELDESLQVAER
(644.8-802.4), EVAFDLEIPK (580.8-861.5).

[0028] The phrase "differentially present" or "differentially expressed"
refers to different in the quantity or intensity of a marker present in a
sample taken from patients having Alzheimer's disease as compared to a
comparable sample taken from patients who do not have Alzheimer's
disease. For example, a protein, polypeptide or peptide is differentially
expressed between the samples if the amount of the protein, polypeptide
or peptide in one sample is significantly different (i.e., p<0.05)
from the amount of the protein, polypeptide or peptide in the other
sample. Further, a peptide ion transition (a "transition," described
below) is differentially present between the samples if the intensity of
the transition is significantly different (i.e., p<0.05) from the
intensity of the transition in the other sample. It should be noted that
if the protein, polypeptide, transition or other marker is detectable in
one sample and not detectable in the other, then such a marker can be
considered to be differentially present.

[0029] To increase the sensitivity of protein detection, a blood, plasma
or serum sample may be initially processed to by any suitable method
known in the art. In one embodiment, blood proteins may be initially
processed by a glycocapture method, which enriches for glycosylated
proteins, allowing quantification assays to detect proteins in the high
pg/ml to low ng/ml concentration range. Example methods of glycocapture
are described in detail in U.S. Pat. No. 7,183,188, issued Jun. 3, 2003;
U.S. Patent Application Publication No. 2007/0099251, published May 3,
2007; U.S. Patent Application Publication No. 2007/0202539, published
Aug. 30, 2007; U.S. Patent Application Publication No. 2007/0269895,
published Nov. 22, 2007; and U.S. Patent Application Publication No.
2010/0279382, published Nov. 4, 2010, all of which are hereby
incorporated by reference in their entirety, as if fully set forth
herein. In another embodiment, blood proteins may be initially processed
by a protein depletion method, which allows for detection of commonly
obscured biomarkers in samples by removing abundant proteins. In one
embodiment, the protein depletion method is a GenWay depletion method.

[0031] In one embodiment, differential expression or differential presence
of the proteins of the panel is quantified by a mass spectrometry method.
The use of mass spectrometry, in accordance with the disclosed methods
and Alzheimer's disease specific panels provides information on not only
the mass to charge ratio (m/z ratio) of ions generated from a sample and
the relative abundance of such ions. Under standardized experimental
conditions, the abundance of a noncovalent biomolecule-ligand complex ion
with the ion abundance of the noncovalent complex formed between a
biomolecule and a standard molecule, such as a known substrate or
inhibitor is compared. Through this comparison, binding affinity of the
ligand for the biomolecule, relative to the known binding of a standard
molecule and the absolute binding affinity may be determined.

[0032] A variety of mass spectrometry systems can be employed for
identifying and/or quantifying Alzheimer's disease biomarkers or
Alzheimer's disease biomarker panels in biological samples. In some
embodiments, analytes may be quantified by liquid chromatography-mass
spectrometry (LC-MS) using eXtracted Ion Chromatograms (XIC). Data are
collected in full MS scan mode and processed post-acquisition, to
reconstruct the elution profile for the ion(s) of interest, with a given
m/z value and a tolerance. XIC peak heights or peak areas are used to
determine the analyte abundance.

[0033] In other embodiments, quantification of analytes is achieved by
selected ion monitoring (SIM) performed on scanning mass spectrometers,
by restricting the acquisition mass range around the m/z value of the
ion(s) of interest. The narrower the mass range, the more specific the
SIM assay. SIM experiments are more sensitive than XICs from full scans
because the MS is allowed to dwell for a longer time over a small mass
range of interest. Several ions within a given m/z range can be observed
without any discrimination and cumulatively quantified; quantification is
still performed using ion chromatograms.

[0034] In other embodiments, selected reaction monitoring (SRM) is used.
SRM exploits the capabilities of triple quadrupole (QQQ) MS for
quantitative analysis of an analyte. SRM is a non-scanning technique,
generally performed on triple quadrupole (QQQ) instruments in which
fragmentation is used as a means to increase selectivity. In SRM, the
first and the third quadrupoles act as filters to specifically select
predefined m/z values corresponding to the peptide ion and a specific
fragment ion of the peptide, whereas the second quadrupole serves as
collision cell. In SRM experiments, two mass analyzers are used as static
mass filters, to monitor a particular fragment ion of a selected
precursor ion. The selectivity resulting from the two filtering stages
combined with the high-duty cycle results in quantitative analyses with
unmatched sensitivity. The specific pair of m/z values associated with
the precursor and fragment ions selected is referred to as a `transition`
(e.g., 673.5/534.3). Several such transitions (precursor/fragment ion
pairs) are monitored over time, yielding a set of chromatographic traces
with the retention time and signal intensity for a specific transition as
coordinates.

[0035] Multiplexed SRM transitions can be measured within the same
experiment on the chromatographic time scale by rapidly cycling through a
series of different transitions and recording the signal of each
transition as a function of elution time. The method, also referred to as
multiple reaction monitoring mass spectrometry (MRM), allows for
additional selectivity by monitoring the chromatographic co-elution of
multiple transitions for a given analyte.

[0036] In some embodiments, an MRM-triggered MS/MS (MRM-MS/MS) method was
used to develop an MRM assay for selection and quantification of target
proteins associated with Alzheimer's disease. For each target protein,
several peptides were selected based on previous identification or
presence in the public peptide MS/MS spectra databases TheGPM,
PeptideAtlas and HUPO. The MRM-MS/MS method was developed by calculating
for each peptide the precursor mass of the doubly and triply charged
peptide ions and the first y fragment ion with an m/z greater than m/z
(precursor)+20 Da. If these calculated transitions were observed during
the MRM scan, a full MS/MS spectrum of the precursor peptide ion was
acquired. The two most intense b or y fragments in the MS/MS spectrum for
each peptide were recorded. Then, the two most suitable peptides for the
MRM assay were selected based on observed signal intensity and origin of
the peptide. FIG. 5 is an illustration of selected peptides (Target
Peptide A, Target Peptide B) having known masses (P1 mass `A` and P1 mass
`B`) and transitions (m1, m2, n1, n2) for a target protein X.

[0037] Based on the peptide and transition selection described above, the
MRM assay used in accordance with the methods for diagnosing Alzheimer's
disease described herein measures the intensity of the four transitions
that correspond to the selected peptides associated with each targeted
protein. The achievable limit of quantification (LOQ) may be estimated
for each peptide according to the observed signal intensities during this
analysis. For example, for a set of target proteins associated with
Alzheimer's disease (A1BG, APOA4, APOD, ARSA, ATP2A2, BDNF, CACNB2,
CALML3, CDH5, CLU, COL18A1, COL1A2, CPN1, CSF1R, EPB41, EPHA8, F13A1,
GALR3, GC, GNAQ, GPR113, GRIN2A, GRN, GSN, HPX, INADL, ITIH1, ITIH2,
Kng1, LAMB2, LRP8, LTBP1, MMP16, MPDZ, MTOR, NMB, NTRK2, PACSIN1, PARD3,
PKDREJ, PON1, PTPRB, SEMG1, SERPINA3, SERPINA4, SERPINF1, SNCB, SYTL4,
TMPRSS2 and VTN), the estimated LOQ for the most intense peptide for each
Alzheimer's disease-related protein is shown in FIG. 11.

[0038] The intensity for each of the four transitions associated with the
Alzheimer's disease panels are measured by MRM assay and compared between
a cohort of Alzheimer's disease patient samples and a cohort of control
patient samples. A control patient may be an individual who has cognitive
impairment due to the normal effects of aging or who has no cognitive
impairment. An individual transition intensity in the cohort of
Alzheimer's disease patient samples that is significantly different than
the corresponding individual transition intensity in the cohort of
control patient samples is selected as a significant transition
biomarker. The protein that corresponds to the significant transition
biomarker is designated as a protein in an Alzheimer's disease panel.

[0039] To determine their diagnostic performance, a receiver operating
characteristic (ROC) curve was generated for each significant transition
biomarker identified above. A "receiver operating characteristic (ROC)
curve" is a generalization of the set of potential combinations of
sensitivity and specificity possible for predictors. A ROC curve is a
plot of the true positive rate (sensitivity) against the false positive
rate (1-specificity) for the different possible cut-points of a
diagnostic test. FIGS. 7 and 9 are a graphical representation of the
functional relationship between the distribution of a biomarker's or a
panel of biomarkers' sensitivity and specificity values in a cohort of
diseased subjects and in a cohort of non-diseased subjects. The area
under the curve (AUC) is an overall indication of the diagnostic accuracy
of (1) a biomarker or a panel of biomarkers and (2) a receiver operating
characteristic (ROC) curve. AUC is determined by the "trapezoidal rule."
For a given curve, the data points are connected by straight line
segments, perpendiculars are erected from the abscissa to each data
point, and the sum of the areas of the triangles and trapezoids so
constructed is computed.

[0040] Having described the invention with reference to the embodiments
and illustrative examples, those in the art may appreciate modifications
to the invention as described and illustrated that do not depart from the
spirit and scope of the invention as disclosed in the specification. The
examples are set forth to aid in understanding the invention but are not
intended to, and should not be construed to limit its scope in any way.
The examples do not include detailed descriptions of conventional
methods. Such methods are well known to those of ordinary skill in the
art and are described in numerous publications. Further, all references
cited above and in the examples below are hereby incorporated by
reference in their entirety, as if fully set forth herein.

Example 1

Generation and Performance of an Alzheimer's Disease Panel

[0041] Sample Processing.

[0042] A set of 130 blood plasma samples were obtained from a cohort of
untreated Alzheimer's disease patients ("the DATU samples;" n=21), a
cohort of Alzheimer's disease patients that were treated with
donepezil/Aricept® ("the DATT samples;" n=31), a cohort of patients
with mild cognitive impairment ("the MDI samples;" n=39) and a cohort of
normal elderly control patients that represent a normal aging brain ("the
NEC samples;" n=39). In addition, 11 tissue test samples were obtained
from neurosurgical controls ("the NC samples;" n=10) and from subjects
with Alzheimer's disease ("the NJ samples;" n=1). Neurosurgical controls
were obtained from patients undergoing neurosurgical treatment for deep
seated tumors, for which removal of apparently normal tissue was a
necessary part of the surgical procedure. The samples were initially
processed by a GenWay depletion method as described above. The enriched
target proteins were then subjected to an MRM as discussed below.

[0045] A panel of 50 proteins was targeted by an MRM assay as described
above. From these 50 target proteins, 100 peptides and 200 transitions
were selected (each peptide had two transitions). Three replicate MRM
analyses were performed to detect presence or expression of the proteins
corresponding to the transitions. A high ranking protein approach was
used to determine the diagnostic importance of the detected proteins
based on discovery studies and biomarkers cited in the literature (see
Pubmed associations and representative references in Table 1, below).

[0046] The intensities of each transition were compared between the
Alzheimer's disease samples and the control samples (Mann-Whitney
U-test). For each target protein, the two transitions having the highest
intensity were compared to determine if the target protein distinguished
diseased samples from normal samples or normal aged samples from the
aging brain. Specifically, the two highest transition intensity
measurements for each target protein in the Alzheimer's disease samples
were compared to the two highest transition intensity measurements for
each target protein in the control samples. A transition was considered
to be significant if the p value was less than 0.05. Fourteen transitions
were found to be significant between Alzheimer's disease and control
samples, corresponding to 7 protein biomarkers. Table 1, shows the
biomarker proteins identified. Examples of significant transition
intensity determinations are shown in FIG. 7 (which corresponds to F13A1
transitions LIASMSSDSLR (590.3-1066.3) (A), LIASMSSDSLR (590.3-953.2)
(B), STVLTIPEIIIK, transition 1 (C) and STVLTIPEIIIK, transition 2 (D)).

[0048] Next, a receiver operating characteristic (ROC) curve was generated
for each significant transition to determine its individual diagnostic
performance. The ROCs are shown in FIG. 8. Briefly, transition
LIASMSSDSLR (590.3-1066.3) had an AUC of 0.73, transition IQNILTEEPK
(592.8-829.4) had an AUC of 0.72, transition LIASMSSDSLR (590.3-953.2)
had an AUC of 0.71, transition IQNILTEEPK (592.8-943.4) had an AUC of
0.70, transition GSLVQASEANLQAAQDFV (1002.5-1448.6) had an AUC of 0.66,
transition EIQNAVNGVK (536.3-417.2) had an AUC of 0.66, transition
VLNQELR (436.2-659.3) had an AUC of 0.63, transition GSLVQASEANLQAAQDFVR
(1002.5-1232.6) had an AUC=0.64, transition TGAQELLR (444.2-530.3) had an
AUC of 0.65, transition ALVQQMEQLR (608.3-932.5) had an AUC of 0.67,
transition TGAQELLR (444.2-658.4) had an AUC of 0.64, transition
ELDESLQVAER (644.8-802.4) had an AUC of 0.66, transition VLNQELR
(436.2-772.4) had an AUC of 0.61 and transition EVAFDLEIPK (580.8-861.5)
had an AUC of 0.66.

[0049] Each individual transition's performance showed modest diagnostic
potential, the performance of all 7 proteins of the Alzheimer's disease
panel was measured based on the combined performance of the 14
transitions. FIG. 9 shows the ROC for the 7-protein biomarker panel based
on the combined performance of the 14 transitions. The AUC (AUC=0.82)
based on a sensitivity of 67% and a specificity of 85%, showed an
improved performance for the 7-protein biomarker panel as compared to any
of the individual transition performances.

[0050] An additional ROC was generated for a 3-protein Alzheimer's disease
panel (GSN, F13A1 and PON1) based on the combined performance of 8
transitions (see FIG. 10) representing 4 peptides (TGAQELLR, LIASMSSDSLR,
IQNILTEEPK, STVLTIPEIIIK). Like the combined performance of the 14
transitions discussed above, the combined performance of 8 transitions
(AUC=0.80) was improved over the individual transition performances and
the AUC. These results illustrate that the combined performance of the
proteins and their transitions is greater than the sum of the individual
markers.